Abstract: Techniques for tuning model parameters to optimize online content are disclosed herein. In some embodiments, a computer system receives logged data for cohorts of users, where the logged data of each one of the plurality of cohorts comprises a number of impressions of online content to the cohort, parameter values applied to objective functions of a model used in selecting the online content for the impressions, contribution actions by the cohort directed towards the online content, and clicks by the cohort directed towards the online content. The computer system, for each cohort, selects one of the parameter values for each objective function based on the logged data. The computer system then selects at least one content item for display to a target user based on the model using the parameter values corresponding to the cohort of the target user.

Abstract: In an example embodiment, a machine learned model is used to determine whether to send a notification for a feed object to a user. This machine learned model is optimized not just based on the likelihood that the notification will cause the user to interact with the feed object, but also the likely short-term and long-term impacts of the user interacting with the feed object. This machine learned model factors in not only the viewer's probability of immediate action, such as clicking on a feed object, but also the probability of long-term impact, such as the display causing the viewer to contribute content to the network or the viewer's response encouraging more people to contribute content to the network. As such, the machine learned model is optimized not just on notification interactivity but also on feed objects interactivity.

Abstract: Techniques for automatically identifying a primary objective for a multi-objective optimization problem are provided. In one technique, an experiment is conduct and results of the experiment involving different values of a model parameter are tracked and stored. Multiple metrics are generated based on the results. For each metric, a maximum or minimum value of the metric given a particular value of the model parameter is determined and a variance associated with the metric is determined based on the maximum or minimum value. A metric that is associated with the lowest variance among the multiple metrics is identified. The identified metric is used as a primary metric in a multi-objective optimization problem.

Abstract: In an example embodiment, a machine learned model is used to determine whether to send a notification for a feed object to a user. This machine learned model is optimized not just based on the likelihood that the notification will cause the user to interact with the feed object, but also the likely short-term and long-term impacts of the user interacting with the feed object. This machine learned model factors in not only the viewer's probability of immediate action, such as clicking on a feed object, but also the probability of long-term impact, such as the display causing the viewer to contribute content to the network or the viewer's response encouraging more people to contribute content to the network. As such, the machine learned model is optimized not just on notification interactivity but also on feed objects interactivity.

Abstract: Techniques for reducing delay in broadcasting content over a network using an inverted fan-out process are disclosed herein. In some embodiments, a computer-implemented method comprises: in response to an activity associated with content being performed by a user on an online service, detecting that the activity has been performed: identifying a plurality of recipient users in response to the detecting; and for each one of the plurality of recipient users, transmitting a notification of the activity to a destination associated with the recipient user in response to the identifying of the recipient users, the notification comprising an indication of the content, and the transmitting of the notification of the activity being performed without waiting for the recipient user to navigate to a web page of the online service on a computing device or for the recipient to open a mobile application of the online service on a mobile device.